The Backbone of Decision Support Systems: The Sensor to Decision Chain

Introduction

The World Meteorological Organization expects a global temperature increase of 3°C caused by climate change (World Meteorological Organization, 2018). This increases the probability for the occurrence of critical natural events, such as floods, dry periods (resulting in forest fires) or heatwaves. To face these challenges, the United Nations have called to intensify the development of early warning systems (UNISDR, 2005). In 2015, the call was renewed, now also including chained disasters (UNISDR, 2015). Through the support of early warning and risk management systems, authorities aim to limit the impact of natural disaster crises.

In this paper, we propose a generic approach to improve the quality of decision support and foster early warning systems. Our method, called the “sensor to decision chain,” covers the steps from sensor data acquisition¹, semantic data analysis, data integration and eventual decision support. The chain forms the basis of an integration framework supported by a variety of cutting-edge technologies. Therefore, the main goal of the framework is to support authorities through a decision support system that implements the sensor to decision chain.

The following subsections describe each step of the sensor to decision chain and present appropriate technologies for its implementation. The rest of this paper is structured as follows: Section “Background” discusses existing decision support workflows and respective implementations; “Motivation” presents the beAWARE project, which serves as a practical example of our implementation and offers the possibility to test our approach in three large-scale pilots; the general approach is discussed in “The sensor to decision chain” section, followed by an thorough description of each step. Finally, “Conclusion” summarizes our findings and discusses directions for further research.